This invention relates to highway traffic incident detection, and more particularly, to a system and method for collecting and analyzing traffic data to detect incidents, the method including an algorithm incorporating neural networks and fuzzy logic techniques.
Probably the most important problem in urban freeway traffic operations is timely detection of unscheduled incidents such as accidents and vehicle breakdowns. An area wide system of strategically placed traffic sensors (e.g., video cameras) could be used to monitor traffic conditions. While humans can readily detect incidents, the work force required to completely monitor an urban road network rapidly becomes cost prohibitive. In place of extensive coverage requiring a large staff continuously watching TV monitors and monitoring data reports, many transportation departments rely on a relatively small number of "motoristaid" vehicles which roam critical highway segments around the network.
A major advantage of "motorist aid" methodologies is that a vehicle is on the spot, and for simple incidents (e.g., a vehicle out-of-fuel), immediate assistance can be rendered and the incident cleared away. Other times (e.g., an accident or mechanical breakdown), the aid vehicle operator can call for appropriate assistance. The major disadvantage of this approach is the geographic coverage required, and the chance the motorist aid vehicle is in the right place at the right time. This is a function of the number of aid vehicles deployed and the miles of road covered.
To insure the most efficient traffic flow possible for a given road network, the most important incidents to detect are those involving stopped vehicles. Rapid detection of these situations and early removal of the vehicles involved is most critical for efficient movement of traffic over the road system. A detection system which automatically detects highway incidents is thus an important need, and this has led the transportation industry to seek automatic incident detection mechanisms using data derived from measurements made by sensors deployed throughout the road system.
The process for determining the presence of an incident is two-fold. The first step is a determination of congestion. Next, an analysis of the congestion determines if its cause is an incident. All of the algorithms now in use are empirical, and most rely on the two variables, volume and occupancy. This is a historical preference since volume and occupancy are the measurements available from inductive loop sensors. There are basically two types of detection algorithms, viz:
(1) those that rely only on the measurement from one sensor station; and PA1 (2) those that use a comparison method of the readings from two sensor stations spatially separated along the highway. PA1 (1) an increase in occupancy upstream of an incident (and where speed is used, a drop in vehicle speed); and PA1 (2) a drop in downstream occupancy together with an increase in vehicle speed. PA1 (1) the complexity of distinguishing an incident from recurrent congestion; and PA1 (2) the difficulty of adjusting for incident related changes in traffic operation because of factors such as weather. PA1 the provision of such a system used with current traffic sensors such as Insight.TM.; PA1 the provision of such a system in which the sensors "learn" and self-calibrate themselves in an unsupervised fashion, real-time, and in the field, this learning and self-calibration occurring both initially when a sensor is located in place, and thereafter; PA1 the provision of such a system to employ a neural network for performing unsupervised learning about local traffic flow; PA1 the provision of such a system in which the sensor makes a determination, in-situ, that a flow anomaly has occurred, and which reports the anomaly to a traffic control center, thereby to lessen the burden on a network monitoring staff by alerting them only when an incident is detected; PA1 the provision of such a system to employ an algorithm utilizing derived traffic parameters in making an anomaly determination; PA1 the provision of such a system in which parameters used by the algorithm may include parameters such as traffic volume, lane speed, lane occupancy, and/or vehicle headway; PA1 the provision of such a system in which the algorithm further uses fuzzy logic in determining whether or not there is a traffic flow anomaly; PA1 the provision of such a system in which the algorithm uses "day of the week" and "time of the day" in determining whether or not there is an anomaly; PA1 the provision of such a system in which the above factors are seasonally dependent and the algorithm incorporates such dependence; PA1 the provision of such a system in which the algorithm makes anomaly determinations on a lane by lane basis, as well as an overall road segment basis; and, PA1 the provision of such a system in which the algorithm associates, with each day of the week in a which a holiday or other special event occasion occurs, a degree of specialness that takes into account the impact of the holiday or special event on normal traffic flow.
The latter are known as comparative algorithms and generally expect
Operating conditions, duration of the incident, detector spacing, and location of the incident are critical for comparative algorithms. Within the two types of algorithms, there are generally two classes of algorithms, those that use instantaneous measurements integrated over a short period of time (e.g. 30 seconds) and those that use a filter methodology, such as a recursive linear filter to in some way balance measurement uncertainty with the fundamental noise in the underlying generalized pattern flow assumptions.
The comparison method relies upon the ability of two detectors to communicate, which intrinsically increases cost of the system and reduces reliability. The prevailing wisdom is that the comparison method is better than a single station because of the latter's tendency to generate excessive false alarms.
The two main problems in developing a single station algorithm are:
Progress has been made, however, in single station detectors because of the recognition that a single station detector is economically better, since it does not require continuous communication between two sensors. An example is the McMaster algorithm which uses flow and occupancy to determine the presence of an incident. This algorithm has been continuously improved and now includes a speed input when such is available from the detector.
Co-pending U.S. patent application Ser. No. 08/531,467 describes a non-imaging traffic sensor system which is useful in monitoring single lane traffic. Co-pending U.S. patent application Ser. No. 08/965,942 describes a video-based traffic sensor system which is useful in monitoring multiple lane traffic flow. These two sensors, like most state of the art sensors, provide not only volume and occupancy data but also speed, headway, and link travel time data. This invention exploits the added dimensionality of traffic flow parameters to more accurately detect the occurrence of traffic incidents.